The Monitoring of Machine Tool Working State Based on Load Current Signal

2010 ◽  
Vol 37-38 ◽  
pp. 1512-1515 ◽  
Author(s):  
Guang Lin Yu ◽  
Guo Fu Li
Keyword(s):  
Working Conditions ◽  
Machine Tools ◽  
Working Condition ◽  
Wavelet Packet ◽  
Analytical Signal ◽  
Wavelet Packet Decomposition ◽  
Machine Condition ◽  
Current Signal ◽  
Motor Current ◽  
Machine Condition Monitoring

According to the characteristic of machine tools such as complex driving chain and enclosed housing, this paper selects current signal which is easy to sample as the analytical signal. As the machines tools use different driving chain in different work state, this will affect motor current of machine tools; that is, the characteristics under different working conditions will be included in the current signal. This paper chose wavelet packet decomposition to analyze the current signal, then extracted wavelet packet coefficients of different frequency bands, by the change of wavelet packet coefficient to determine the machine's working condition. From the analysis of lathe current signal sampled in the experiment, it indicates the validity of wavelet packet coefficients as a feature quantity of the machine condition monitoring.

Condition Feature Extraction of Machine Tools Based on Wavelet Packet Energy Spectrum Analysis and Bispectrum Analysis of Current Signal

2011 ◽  
Vol 101-102 ◽  
pp. 847-850 ◽  
Author(s):  
Teng Fei Fang ◽  
Guo Fu Li
Keyword(s):  
Feature Extraction ◽  
Energy Spectrum ◽  
Real Time ◽  
Spectrum Analysis ◽  
Machine Tools ◽  
Signal Analysis ◽  
Wavelet Packet ◽  
Current Signal ◽  
Load Current ◽  
Working Status

Based on the study of the characteristics of load current signal, this article develops a method to extract features that can be use to distinguish the different working status of machine tools in real-time manner. The features are extracted from wavelet packet energy spectrum and bispectrum of the load current signal, and thus can take advantages of both wavelet packet transforms and bispectrum in signal analysis. Experimental results show that, compared with the features extracted from wavelet packet energy spectrum or bispectrum alone, the features extracted by applying the proposed method can provide better performance in term of identifying the machine working status.

An Experimental Study on Working State Recognition of Machine Tools

2012 ◽  
Vol 201-202 ◽  
pp. 707-710
Author(s):  
Teng Fei Fang ◽  
Guo Fu Li ◽  
Lei Wang ◽  
Hong Bin Li ◽  
Wei Guo
Keyword(s):  
Frequency Analysis ◽  
Machine Tools ◽  
Wavelet Packet ◽  
Current Signal ◽  
Wavelet Packet Analysis ◽  
Time Frequency Analysis ◽  
State Recognition ◽  
Time Frequency ◽  
Different Characteristics ◽  
One Machine

In order to obtain the real-time working state of machine tools, this experiment extracted the characteristics of machine tools using joint time-frequency analysis and wavelet packet analysis for the total current signal collected, to distinguish which machine is running. First, use joint time-frequency analysis on signal of a single machine to get different characteristics. And find some frequency points with amplitude changing significantly, preparing for the subsequent experiment. Then use wavelet packet analysis on the total signal of more than one machine, finding more obvious characteristics of the different machines with different speeds. Thus it is easy to identify which machine is working. By this experiment, we can save labor, improve efficiency and integrate information in system conveniently.

scholarly journals Machine Condition Classification by Using Wavelet Packet Decomposition and Multi-Scale Entropy

2011 ◽  
Vol 2-3 ◽  
pp. 743-748
Author(s):  
Hong Kun Li ◽  
Shu Ai Zhou ◽  
Yu Zhen Chen
Keyword(s):  
Frequency Band ◽  
Working Condition ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Operating Conditions ◽  
Wavelet Packet Decomposition ◽  
Multi Scale ◽  
Series Of Experiments

A new condition classification method is put forward based on the analysis of vibration signals. Machine working condition can be recognized by the combination of wavelet packet decomposition (WPD) and multi-scale entropy (MSE). Firstly, vibration signal of machine is decomposed by wavelet packet with the appropriate decomposition layer. Then, each sub-signal in different frequency band is analyzed with the multi-scale entropy. Through analyzing the multi-scale entropy distribution curves of sub-signals for different operating conditions in each frequency band, entropy of certain frequency bands and scales will be chosen as the feature vector, which is used to distinguish different machine conditions. This method presents a novel perspective for rolling bearing default diagnosis and is tested to be very effective to classify different bearing operating conditions through series of experiments.

scholarly journals Reliability Evaluation of NC Machine Tools considering Working Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Hongzhou Li ◽  
Zhaojun Yang ◽  
Binbin Xu ◽  
Chuanhai Chen ◽  
Yingnan Kan ◽  
...  
Keyword(s):  
Working Conditions ◽  
Machine Tools ◽  
Working Condition ◽  
Evaluation Model ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Reliability Evaluation ◽  
Reliability Level ◽  
Nc Machine ◽  
Nc Machine Tools

Reliability evaluation is the basis for reliability design of NC machine tools. Since traditional reliability evaluation methods do not consider the working conditions’ effects on reliability, there is a great error of a result of a traditional method compared with an actual value. A new reliability evaluation model of NC machine tools is proposed based on the Cox proportional hazards model, which describes the mathematical relation between the working condition covariates and the reliability level of NC machine tools. Firstly, the coefficients of working condition covariates in the new reliability evaluation model are estimated by the partial likelihood estimation method; secondly, the working condition covariates which have no effects on the reliability of NC machine tools are eliminated by the likelihood ratio test; then parameters of the baseline failure rate function are estimated by the maximum likelihood estimation method. Thus, the reliability evaluation model of NC machine tool is obtained under different working conditions and the reliability level of NC machine tools is obtained. Case study shows that the proposed method could establish the relation between the working condition covariates and the reliability level of NC machine tools, and it would provide a new way for the reliability evaluation of NC machine tools.

scholarly journals Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1997
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yujin Du ◽  
Dongmei Xu
Keyword(s):  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Processing Technique ◽  
Annual Rainfall ◽  
Wavelet Packet Decomposition ◽  
Rainfall Forecasting ◽  
Promising Alternative ◽  
Forecasting Models ◽  
Precipitation Prediction

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.

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